The cervicovaginal microbiome of pregnant people living with HIV on antiretroviral therapy in the Democratic Republic of Congo: A Pilot Study and Global Meta-Analysis
Kimberley S Ndlovu & Ricardo R Pavan
2025-02-18
Figure scripts for the paper, “The cervicovaginal microbiome of pregnant people living with HIV on antiretroviral therapy in the Democratic Republic of Congo: A Pilot Study and Global Meta-Analysis”
## Loading required package: pacman## Loading required package: veganEx1 Figure 1
1.1 B. Heatmap of RCLR abundances in the CQI-PMTCT cohort
## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'## Also defined by 'tidytree'## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'## Also defined by 'tidytree'## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'## Also defined by 'tidytree'## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'## Also defined by 'tidytree'## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'## Also defined by 'tidytree'## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'## Also defined by 'tidytree'## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'## Also defined by 'tidytree'## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'## Also defined by 'tidytree'## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'## Also defined by 'tidytree'## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'## Also defined by 'tidytree'## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'## Also defined by 'tidytree'## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'## Also defined by 'tidytree'## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'## Also defined by 'tidytree'## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'## Also defined by 'tidytree'## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'## Also defined by 'tidytree'## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'## Also defined by 'tidytree'## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'## Also defined by 'tidytree'## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'## Also defined by 'tidytree'## `summarise()` has grouped output by 'sampleID'. You can override using the
## `.groups` argument.
2 Figure 2: Ecological statistics of the CVMB in the CQI-PMTCT cohort.
2.1 A.CQI-PMTCT cohort:Shannon diversity by CST
## Warning in BootstrapFun.abun(x = x, FunName, datatype, B): The Bootstrap
## community has only one species. Estimation is not robust.
## Warning in BootstrapFun.abun(x = x, FunName, datatype, B): The Bootstrap
## community has only one species. Estimation is not robust.##
## Kruskal-Wallis rank sum test
##
## data: shannon$`Shannon Entropy` by shannon$CST
## Kruskal-Wallis chi-squared = 14.456, df = 3, p-value = 0.002346## # A tibble: 6 × 9
## .y. group1 group2 n1 n2 statistic p p.adj p.adj.signif
## * <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
## 1 Shannon Entr… II III 2 43 0.587 5.57e-1 1 ns
## 2 Shannon Entr… II IV 2 35 1.71 8.73e-2 0.524 ns
## 3 Shannon Entr… II V 2 1 0.174 8.62e-1 1 ns
## 4 Shannon Entr… III IV 43 35 3.60 3.24e-4 0.00195 **
## 5 Shannon Entr… III V 43 1 -0.210 8.34e-1 1 ns
## 6 Shannon Entr… IV V 35 1 -1.02 3.10e-1 1 ns## Warning: Use of `` shannon$`Shannon Entropy` `` is discouraged.
## ℹ Use `Shannon Entropy` instead.
2.2 B.CQI-PMTCT cohort:PCA by CST
## ANOSIM Results:
##
## Call:
## anosim(x = dist_matrix, grouping = grouping_var)
## Dissimilarity: euclidean
##
## ANOSIM statistic R: 0.2058
## Significance: 0.001
##
## Permutation: free
## Number of permutations: 999
##
##
## Summary of ANOSIM Results:
##
## Call:
## anosim(x = dist_matrix, grouping = grouping_var)
## Dissimilarity: euclidean
##
## ANOSIM statistic R: 0.2058
## Significance: 0.001
##
## Permutation: free
## Number of permutations: 999
##
## Upper quantiles of permutations (null model):
## 90% 95% 97.5% 99%
## 0.0435 0.0571 0.0725 0.0879
##
## Dissimilarity ranks between and within classes:
## 0% 25% 50% 75% 100% N
## Between 3 1010 1838 2563 3240 1741
## II 410 410 410 410 410 1
## III 1 363 916 1706 3234 903
## IV 94 1354 2051 2669 3236 595
## V NA NA NA NA NA 0
## 'nperm' >= set of all permutations: complete enumeration.## Set of permutations < 'minperm'. Generating entire set.## Pairwise Anosim Results:
## pairs anosimR p.value p.adj
## 1 III.vs.IV 0.2188043 0.0010000 0.006
## 2 III.vs.II 0.5497273 0.0240000 0.144
## 3 III.vs.V -0.4749800 0.9510000 1.000
## 4 IV.vs.II 0.1703739 0.2560000 1.000
## 5 IV.vs.V -0.5316206 0.9450000 1.000
## 6 II.vs.V 0.0000000 0.6666667 1.000## Warning in ord_calc(., method = "PCA", scale_cc = FALSE): * data provided to ord_calc is a phyloseq object, not a psExtra.
## * Consider using tax_agg and/or tax_transform before ordinating.## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'## Also defined by 'tidytree'## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'## Also defined by 'tidytree'## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'## Also defined by 'tidytree'## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'## Also defined by 'tidytree'## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'## Also defined by 'tidytree'## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'## Also defined by 'tidytree'## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'## Also defined by 'tidytree'## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'## Also defined by 'tidytree'## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'## Also defined by 'tidytree'## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'## Also defined by 'tidytree'## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'## Also defined by 'tidytree'## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'## Also defined by 'tidytree'## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'## Also defined by 'tidytree'## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'## Also defined by 'tidytree'
2.3 C.CQI-PMTCT cohort:Shannon diversity by ART regimen
##
## Kruskal-Wallis rank sum test
##
## data: shannon$`Shannon Entropy` by shannon$ART_regimen
## Kruskal-Wallis chi-squared = 0.72658, df = 2, p-value = 0.6954## # A tibble: 3 × 9
## .y. group1 group2 n1 n2 statistic p p.adj p.adj.signif
## * <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
## 1 Shannon Entropy TDF_3TC TDF_3… 1 19 0.293 0.770 0.938 ns
## 2 Shannon Entropy TDF_3TC TDF_3… 1 59 0.0776 0.938 0.938 ns
## 3 Shannon Entropy TDF_3TC… TDF_3… 19 59 -0.842 0.400 0.938 ns
2.4 D.CQI-PMTCT cohort: PCA by ART
## ANOSIM Results:
##
## Call:
## anosim(x = dist_matrix, grouping = grouping_var)
## Dissimilarity: euclidean
##
## ANOSIM statistic R: -0.07724
## Significance: 0.835
##
## Permutation: free
## Number of permutations: 999
##
##
## Summary of ANOSIM Results:
##
## Call:
## anosim(x = dist_matrix, grouping = grouping_var)
## Dissimilarity: euclidean
##
## ANOSIM statistic R: -0.07724
## Significance: 0.835
##
## Permutation: free
## Number of permutations: 999
##
## Upper quantiles of permutations (null model):
## 90% 95% 97.5% 99%
## 0.101 0.133 0.168 0.181
##
## Dissimilarity ranks between and within classes:
## 0% 25% 50% 75% 100% N
## Between 3 712.5 1469 2201.0 3080 1199
## TDF_3TC NA NA NA NA NA 0
## TDF_3TC_EFV 22 765.5 1482 2162.5 2962 171
## TDF_3TC_TLD 1 826.0 1615 2407.0 3081 1711
## Pairwise Anosim Results:
## pairs anosimR p.value p.adj
## 1 TDF_3TC_EFV.vs.TDF_3TC_TLD -0.0579081 0.761 1
## 2 TDF_3TC_EFV.vs.TDF_3TC -0.3481071 0.816 1
## 3 TDF_3TC_TLD.vs.TDF_3TC -0.3441639 0.840 1## Joining with `by = join_by(sampleID)`
2.5 E.CQI-PMTCT cohort:Shannon diversity by viremia
## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot
## compute exact p-value with ties##
## Wilcoxon rank sum test with continuity correction
##
## data: shannon$`Shannon Entropy` by shannon$Viral_load_enrol_detected
## W = 124, p-value = 0.2853
## alternative hypothesis: true location shift is not equal to 0
2.6 F.CQI-PMTCT cohort: PCA by viremia
## ANOSIM Results:
##
## Call:
## anosim(x = dist_matrix, grouping = grouping_var)
## Dissimilarity: euclidean
##
## ANOSIM statistic R: -0.1062
## Significance: 0.783
##
## Permutation: free
## Number of permutations: 999
##
##
## Summary of ANOSIM Results:
##
## Call:
## anosim(x = dist_matrix, grouping = grouping_var)
## Dissimilarity: euclidean
##
## ANOSIM statistic R: -0.1062
## Significance: 0.783
##
## Permutation: free
## Number of permutations: 999
##
## Upper quantiles of permutations (null model):
## 90% 95% 97.5% 99%
## 0.163 0.225 0.270 0.318
##
## Dissimilarity ranks between and within classes:
## 0% 25% 50% 75% 100% N
## Between 1 281.75 523 806.5 1161 328
## 0 2 312.00 623 925.5 1176 820
## 1 86 275.50 494 690.0 907 28
## Pairwise Anosim Results:
## pairs anosimR p.value p.adj
## 1 0.vs.1 -0.1062471 0.805 0.805## Joining with `by = join_by(sampleID)`
3 Figure 3: Ecological statistics in the integrated datasets
3.0.1 Calculate alpha diversity
## Joining, by = "Sample"
## Joining, by = "OTU"
## Rows: 1851 Columns: 28
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "\t" chr
## (18): sampleID, continent, unsdg_region, country, study, condition, sequ... dbl
## (10): age_years, bmikgm2, Birth_outcoome, Asymptomatic, BV, HIV, Antibio...
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
## Specify the column types or set `show_col_types = FALSE` to quiet this message.## Warning: There were 62 warnings in `mutate()`.
## The first warning was:
## ℹ In argument: `alpha_df = map(data, ~get_asymptotic_alpha(species = .x,
## verbose = FALSE))`.
## ℹ In group 1: `study = "Ho_2021"`.
## Caused by warning in `BootstrapFun.abun()`:
## ! The Bootstrap community has only one species. Estimation is not robust.
## ℹ Run `dplyr::last_dplyr_warnings()` to see the 61 remaining warnings.## Joining with `by = join_by(sample_id)`## # A tibble: 5 × 9
## .y. n statistic df p method p.adj p.adj.signif variable
## <chr> <int> <dbl> <int> <dbl> <chr> <dbl> <chr> <chr>
## 1 value 664 275. 5 2.05e-57 Kruskal-W… 2.05e-57 **** Chao1
## 2 value 664 218. 5 4.85e-45 Kruskal-W… 4.85e-45 **** Simpson…
## 3 value 664 219. 5 2.45e-45 Kruskal-W… 2.45e-45 **** Shannon…
## 4 value 664 218. 5 4.22e-45 Kruskal-W… 4.22e-45 **** InvSimp…
## 5 value 664 165. 5 7.02e-34 Kruskal-W… 7.02e-34 **** Pielou3.1 A.Meta-analysis: Shannon diversity by health status
## Rows: 664 Columns: 8
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (3): sample_id, study, health_status
## dbl (5): Chao1, Simpson Index, Shannon Entropy, InvSimpson, Pielou
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.##
## Wilcoxon rank sum test with continuity correction
##
## data: Shannon Entropy by health_status
## W = 16109, p-value = 2.661e-07
## alternative hypothesis: true location shift is not equal to 0## # A tibble: 1 × 9
## .y. group1 group2 n1 n2 statistic p p.adj p.adj.signif
## * <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
## 1 Shannon Entr… Asymp… HIV 579 85 5.15 2.66e-7 2.66e-7 ****
3.2 B.Meta-analysis: PCA by health status
## ANOSIM Results:
##
## Call:
## anosim(x = dist_matrix, grouping = grouping_var)
## Dissimilarity: euclidean
##
## ANOSIM statistic R: 0.518
## Significance: 0.001
##
## Permutation: free
## Number of permutations: 999
##
##
## Summary of ANOSIM Results:
##
## Call:
## anosim(x = dist_matrix, grouping = grouping_var)
## Dissimilarity: euclidean
##
## ANOSIM statistic R: 0.518
## Significance: 0.001
##
## Permutation: free
## Number of permutations: 999
##
## Upper quantiles of permutations (null model):
## 90% 95% 97.5% 99%
## 0.0501 0.0646 0.0759 0.0899
##
## Dissimilarity ranks between and within classes:
## 0% 25% 50% 75% 100% N
## Between 21.5 116817.0 172938 203640.5 220116 49215
## Asymptomatic 4.5 45543.5 92906 144172.5 220025 167331
## HIV 1230.0 131938.5 174769 200778.8 220066 3570
## Pairwise Anosim Results:
## pairs anosimR p.value p.adj
## 1 Asymptomatic.vs.HIV 0.517979 0.001 0.001## Joining with `by = join_by(sampleID)`
3.3 C.Metaanaly:Shannon by CST
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties##
## Pairwise comparisons using Wilcoxon rank sum test with continuity correction
##
## data: alpha_div_all$`Shannon Entropy` and alpha_div_all$CST
##
## I II III IV
## II 0.026 - - -
## III 1.3e-12 0.122 - -
## IV < 2e-16 1.5e-08 1.0e-10 -
## V 1.5e-08 0.019 0.448 2.2e-06
##
## P value adjustment method: BH## [1] 1.023234e-35## Warning in wilcox.test.default(c(0.668, 0.647, 0.305, 0.688, 0.511, 0.01, :
## cannot compute exact p-value with ties## Warning: Removed 5 rows containing missing values or values outside the scale range
## (`geom_segment()`).
3.4 D.Meta-analysis: PCA by CST
## ANOSIM Results:
##
## Call:
## anosim(x = dist_matrix, grouping = grouping_var)
## Dissimilarity: euclidean
##
## ANOSIM statistic R: 0.3991
## Significance: 0.001
##
## Permutation: free
## Number of permutations: 999
##
##
## Summary of ANOSIM Results:
##
## Call:
## anosim(x = dist_matrix, grouping = grouping_var)
## Dissimilarity: euclidean
##
## ANOSIM statistic R: 0.3991
## Significance: 0.001
##
## Permutation: free
## Number of permutations: 999
##
## Upper quantiles of permutations (null model):
## 90% 95% 97.5% 99%
## 0.0179 0.0228 0.0274 0.0335
##
## Dissimilarity ranks between and within classes:
## 0% 25% 50% 75% 100% N
## Between 277.0 76065.75 125236.5 172619.2 220116 154796
## I 4.5 10842.00 27458.0 63063.0 219071 25425
## II 2296.0 48663.50 94403.0 140282.0 200202 351
## III 4.5 32605.25 75160.0 139758.0 220105 31375
## IV 640.0 122788.50 173113.0 202209.5 220114 7503
## V 269.0 27414.75 59008.5 97577.0 200182 666
## Pairwise Anosim Results:
## pairs anosimR p.value p.adj
## 1 I.vs.IV 0.55501274 0.001 0.01
## 2 I.vs.III 0.39309097 0.001 0.01
## 3 I.vs.V 0.64534997 0.001 0.01
## 4 I.vs.II 0.70507056 0.001 0.01
## 5 IV.vs.III 0.34693783 0.001 0.01
## 6 IV.vs.V -0.05604106 0.839 1.00
## 7 IV.vs.II -0.15375269 0.988 1.00
## 8 III.vs.V 0.25425460 0.001 0.01
## 9 III.vs.II 0.26104714 0.002 0.02
## 10 V.vs.II 0.47210140 0.001 0.01## Joining with `by = join_by(sampleID)`
4 Figure 4: Meta-analysis: Differentially abundant species by health status
4.0.1 perform DAAs
## Imputation approach is used.## Warning: The group variable has < 3 categories
## The multi-group comparisons (global/pairwise/dunnet/trend) will be deactivated## Obtaining initial estimates ...## Estimating sample-specific biases ...## ANCOM-BC2 primary results ...## The sensitivity analysis for the pseudo-count addition ...## checking for condition length disabled!## using all features for denominator## operating in serial mode## computing center with all features## running tests for each MC instance:## |------------(25%)----------(50%)----------(75%)----------|## operating in serial mode## sanity check complete## rab.all complete## rab.win complete## rab of samples complete## within sample difference calculated## between group difference calculated## group summaries calculated## unpaired effect size calculated## summarizing output## operating in serial mode## sanity check complete## rab.all complete## rab.win complete## rab of samples complete## within sample difference calculated## between group difference calculated## group summaries calculated## unpaired effect size calculated## summarizing output## operating in serial mode## sanity check complete## rab.all complete## rab.win complete## rab of samples complete## within sample difference calculated## between group difference calculated## group summaries calculated## unpaired effect size calculated## summarizing output## operating in serial mode## sanity check complete## rab.all complete## rab.win complete## rab of samples complete## within sample difference calculated## between group difference calculated## group summaries calculated## unpaired effect size calculated## summarizing output## operating in serial mode## sanity check complete## rab.all complete## rab.win complete## rab of samples complete## within sample difference calculated## between group difference calculated## group summaries calculated## unpaired effect size calculated## summarizing output## operating in serial mode## sanity check complete## rab.all complete## rab.win complete## rab of samples complete## within sample difference calculated## between group difference calculated## group summaries calculated## unpaired effect size calculated## summarizing output4.1 Figure 4
# box plot only DAA health Status and detect by at least 2 methods
only_health_status <- all_methods %>%
filter(if_any(starts_with("variable"), ~str_detect(., "health_status"))) %>%
filter(score > 1)
daa_spp <- only_health_status %>% distinct(Species) %>% pull(Species)
# load CLR-transformed data
#load('ps_filt_spp_clr_zero.RData') # only CLR
pseq.bac <- ps_filt_spp_clr_zero_adj
meta <- pseq.bac %>% sample_tibble(column.id = "sample_id")
tax_df <- pseq.bac %>% tax_tibble("id")
otu_df <- pseq.bac %>% otu_tibble("id")
ps_melted_clr <- pseq.bac %>% psmelt.dplyr() %>% rename(id = OTU)## Joining, by = "Sample"
## Joining, by = "OTU"color_study <- c(
"PMTCT_CQI" = "red",
"Movassagh_2021" = "green",
"Kervinen_2022" = "#445002",
"Livson_2022" = "lavender",
"Servergnini_2021" = "navy",
"Ho_2021" = "#AA9F0D"
)
ps_melted_clr %>%
filter(id %in% daa_spp) %>%
mutate(id = gsub("f__|_g__|_s__", " ", id),
id = gsub("^ ", "", id),
id = gsub("([^ ]+)( )", "\\1\n", id)) %>%
ggplot(aes(x = id, y = abundance,
fill = health_status)) +
# geom_jitter(aes(color = study),alpha=0.2) +
geom_boxplot(coef = 100, width = 0.5) +
# facet_wrap(vars(id), scales = "free_y") +
scale_fill_manual(values = c("HIV" = "#DC3220", "Asymptomatic" = "#005AB5")) +
# ggtitle("Differentially abundant Species between Asymptomatic and HIV groups") +
scale_color_manual(values = color_study) +
theme_classic() +
labs(y = "Species Abundance (CLR)") +
guides(colour = guide_legend(override.aes = list(alpha = 1))) +
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank()) +
theme(axis.text.x = element_text(size = 12, angle = 90, hjust = 1),
strip.background = element_rect(fill = "white", color = "white"),
strip.text.x = element_text(size = 12),
axis.title.x = element_blank(),
plot.title = element_text(size=20))## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.
5 Figure 5: CQI-PMTCT: Predicted KEGG pathways differentially abundant by CST
5.1 perform DAAs
## Rows: 162 Columns: 85
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (4): KO, pathway_1, pathway_2, pathway_3
## dbl (81): V102, V103, V104, V011, V112, V113, V118, V119, V012, V125, V128, ...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.## Warning in melt.data.table(., variable.factor = FALSE): id.vars and
## measure.vars are internally guessed when both are 'NULL'. All
## non-numeric/integer/logical type columns are considered id.vars, which in this
## case are columns [KO, pathway_1, pathway_2, pathway_3, ...]. Consider providing
## at least one of 'id' or 'measure' vars in future.## `summarise()` has grouped output by 'variable'. You can override using the
## `.groups` argument.## Pseudo-count approach is used.## Rows: 180 Columns: 15
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (8): KO, reject, name, method, pathway_3, pathway_1, pathway_2, pathway_...
## dbl (7): baseMean, log2FoldChange, lfcSE, stat, pvalue, padj, df
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
## `tax_level` is not speficified
## No agglomeration will be performed
## Otherwise, please speficy `tax_level` by one of the following:
##
##
## Obtaining initial estimates ...
##
## Estimating sample-specific biases ...
##
## ANCOM-BC2 primary results ...
##
## The sensitivity analysis for the pseudo-count addition ...
##
## Rows: 255 Columns: 13
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (7): KO, name, method, pathway_3, pathway_1, pathway_2, pathway_3_clean
## dbl (5): lfc, se, W, p, q
## lgl (1): diff
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
## checking for condition length disabled!
##
## using all features for denominator
##
## operating in serial mode
##
## computing center with all features
##
## running tests for each MC instance:## |------------(25%)----------(50%)----------(75%)----------|## operating in serial mode
## sanity check complete
## rab.all complete
## rab.win complete
## rab of samples complete
## within sample difference calculated
## between group difference calculated
## group summaries calculated
## unpaired effect size calculated
## summarizing output
## operating in serial mode
## sanity check complete
## rab.all complete
## rab.win complete
## rab of samples complete
## within sample difference calculated
## between group difference calculated
## group summaries calculated
## unpaired effect size calculated
## summarizing output
## Rows: 178 Columns: 14── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (7): KO, name, method, pathway_3, pathway_1, pathway_2, pathway_3_clean
## dbl (7): rab.all, rab.win.0, rab.win.1, diff.btw, diff.win, effect, overlap
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.## # A tibble: 1 × 9
## KO LinDA ancombc aldex2 score pathway_3 pathway_1 pathway_2 pathway_3_clean
## <chr> <lgl> <lgl> <lgl> <dbl> <chr> <chr> <chr> <chr>
## 1 KO_3 TRUE TRUE FALSE 2 Brite Hi… Brite Hi… Signalin… G Protein-Coup…## Rows: 25 Columns: 9
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (5): KO, pathway_3, pathway_1, pathway_2, pathway_3_clean
## dbl (1): score
## lgl (3): LinDA, ancombc, aldex2
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.5.2 Figure 5
## Rows: 25 Columns: 9
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (5): KO, pathway_3, pathway_1, pathway_2, pathway_3_clean
## dbl (1): score
## lgl (3): LinDA, ancombc, aldex2
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.## [1] "The DL is equal to 0.176470588235294"
## [1] "The DL is equal to 2"
## [1] "The DL is equal to 0.986"
## [1] "The DL is equal to 0.6875"
## [1] "The DL is equal to 6.86111111111111"
## [1] "The DL is equal to 0.166875"
## [1] "The DL is equal to 0.176470588235294"
## [1] "The DL is equal to 0.208333333333333"
## [1] "The DL is equal to 0.05375"
## [1] "The DL is equal to 1.18181818181818"
## [1] "The DL is equal to 1.208125"
## [1] "The DL is equal to 0.235294117647059"
## [1] "The DL is equal to 0.3125"
## [1] "The DL is equal to 0.125"
## [1] "The DL is equal to 0.277777777777778"
## [1] "The DL is equal to 0.176470588235294"
## [1] "The DL is equal to 0.0526315789473684"
## [1] "The DL is equal to 1.333125"
## [1] "The DL is equal to 0.029"
## [1] "The DL is equal to 0.5"
## [1] "The DL is equal to 49.84"
## [1] "The DL is equal to 0.611111111111111"
## [1] "The DL is equal to 0.25"
## [1] "The DL is equal to 0.043"
## [1] "The DL is equal to 0.029"
## [1] "The DL is equal to 0.363636363636364"
## [1] "The DL is equal to 3.38888888888889"
## [1] "The DL is equal to 0.166666666666667"
## [1] "The DL is equal to 0.036231884057971"
## [1] "The DL is equal to 0.375"
## [1] "The DL is equal to 8.25"
## [1] "The DL is equal to 0.3"
## [1] "The DL is equal to 0.4375"
## [1] "The DL is equal to 4.45833333333333"
## [1] "The DL is equal to 0.222222222222222"
## [1] "The DL is equal to 0.205882352941176"
## [1] "The DL is equal to 0.173478260869565"
## [1] "The DL is equal to 0.029"
## [1] "The DL is equal to 0.117647058823529"
## [1] "The DL is equal to 3.25"
## [1] "The DL is equal to 0.777777777777778"
## [1] "The DL is equal to 2.75"
## [1] "The DL is equal to 10.7628571428571"
## [1] "The DL is equal to 0.117647058823529"
## [1] "The DL is equal to 1.29166666666667"
## [1] "The DL is equal to 0.537222222222222"
## [1] "The DL is equal to 0.192307692307692"
## [1] "The DL is equal to 0.222222222222222"
## [1] "The DL is equal to 0.00886666666666666"
## [1] "The DL is equal to 0.5"
## [1] "The DL is equal to 0.5625"
## [1] "The DL is equal to 0.684782608695652"
## [1] "The DL is equal to 0.104375"
## [1] "The DL is equal to 0.270625"
## [1] "The DL is equal to 0.375"
## [1] "The DL is equal to 0.03125"
## [1] "The DL is equal to 0.125"
## [1] "The DL is equal to 0.395625"
## [1] "The DL is equal to 0.388888888888889"
## [1] "The DL is equal to 0.125"
## [1] "The DL is equal to 1.6"
## [1] "The DL is equal to 0.1875"
## [1] "The DL is equal to 1.42857142857143"
## [1] "The DL is equal to 0.272727272727273"
## [1] "The DL is equal to 0.428571428571429"
## [1] "The DL is equal to 0.08"
## [1] "The DL is equal to 0.875"
## [1] "The DL is equal to 2.61111111111111"
## [1] "The DL is equal to 20.8888888888889"
## [1] "The DL is equal to 0.8"
## [1] "The DL is equal to 1.375"
## [1] "The DL is equal to 0.5"
## [1] "The DL is equal to 0.611111111111111"
## [1] "The DL is equal to 6"
## [1] "The DL is equal to 0.00971014492753623"
## [1] "The DL is equal to 0.291875"
## [1] "The DL is equal to 0.0825"
## [1] "The DL is equal to 0.067"
## [1] "The DL is equal to 2.375"
## [1] "The DL is equal to 0.0625"## Joining, by = "Sample"
## Joining, by = "OTU"
6 Figure 6: Meta-analysis: Predicted KEGG pathways differentially abundant by health status
6.1 perform DAAs
## Rows: 130 Columns: 668
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (4): KO, pathway_1, pathway_2, pathway_3
## dbl (664): EU_PRJEB47492_ERR6799751, EU_PRJEB47492_ERR6799752, EU_PRJEB47492...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.## Warning in melt.data.table(., variable.factor = FALSE): id.vars and
## measure.vars are internally guessed when both are 'NULL'. All
## non-numeric/integer/logical type columns are considered id.vars, which in this
## case are columns [KO, pathway_1, pathway_2, pathway_3, ...]. Consider providing
## at least one of 'id' or 'measure' vars in future.## `summarise()` has grouped output by 'variable'. You can override using the
## `.groups` argument.
## Rows: 1851 Columns: 28
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "\t" chr
## (18): sampleID, continent, unsdg_region, country, study, condition, sequ... dbl
## (10): age_years, bmikgm2, Birth_outcoome, Asymptomatic, BV, HIV, Antibio...
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
## Specify the column types or set `show_col_types = FALSE` to quiet this message.## Imputation approach is used.## Rows: 480 Columns: 11
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (4): KO, reject, name, method
## dbl (7): baseMean, log2FoldChange, lfcSE, stat, pvalue, padj, df
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
## `tax_level` is not speficified
## No agglomeration will be performed
## Otherwise, please speficy `tax_level` by one of the following:## Warning: The group variable has < 3 categories
## The multi-group comparisons (global/pairwise/dunnet/trend) will be deactivated## Obtaining initial estimates ...
## Estimating sample-specific biases ...
## ANCOM-BC2 primary results ...
## The sensitivity analysis for the pseudo-count addition ...
## Rows: 525 Columns: 9── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (3): KO, name, method
## dbl (5): lfc, se, W, p, q
## lgl (1): diff
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.checking for condition length disabled!
## using all features for denominator
## operating in serial mode
## computing center with all features
## running tests for each MC instance:## |------------(25%)----------(50%)----------(75%)----------|## operating in serial mode
## sanity check complete
## rab.all complete
## rab.win complete
## rab of samples complete
## within sample difference calculated
## between group difference calculated
## group summaries calculated
## unpaired effect size calculated
## summarizing output
## operating in serial mode
## sanity check complete
## rab.all complete
## rab.win complete
## rab of samples complete
## within sample difference calculated
## between group difference calculated
## group summaries calculated
## unpaired effect size calculated
## summarizing output
## operating in serial mode
## sanity check complete
## rab.all complete
## rab.win complete
## rab of samples complete
## within sample difference calculated
## between group difference calculated
## group summaries calculated
## unpaired effect size calculated
## summarizing output
## operating in serial mode
## sanity check complete
## rab.all complete
## rab.win complete
## rab of samples complete
## within sample difference calculated
## between group difference calculated
## group summaries calculated
## unpaired effect size calculated
## summarizing output
## operating in serial mode
## sanity check complete
## rab.all complete
## rab.win complete
## rab of samples complete
## within sample difference calculated
## between group difference calculated
## group summaries calculated
## unpaired effect size calculated
## summarizing output
## operating in serial mode
## sanity check complete
## rab.all complete
## rab.win complete
## rab of samples complete
## within sample difference calculated
## between group difference calculated
## group summaries calculated
## unpaired effect size calculated
## summarizing output
## Rows: 480 Columns: 10── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (3): KO, name, method
## dbl (7): rab.all, rab.win.0, rab.win.1, diff.btw, diff.win, effect, overlap
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Joining with `by = join_by(KO)`## # A tibble: 19 × 12
## KO LinDA ancombc aldex2 score variable_linda variable_ancombc
## <chr> <lgl> <lgl> <lgl> <dbl> <chr> <chr>
## 1 KO_43 TRUE TRUE TRUE 3 study study
## 2 KO_49 TRUE TRUE TRUE 3 study study
## 3 KO_24 TRUE TRUE TRUE 3 study study
## 4 KO_45 TRUE TRUE TRUE 3 study study
## 5 KO_51 TRUE TRUE TRUE 3 study study
## 6 KO_56 TRUE TRUE TRUE 3 study study
## 7 KO_68 TRUE TRUE TRUE 3 study study
## 8 KO_9 TRUE TRUE TRUE 3 study study
## 9 KO_2 TRUE TRUE TRUE 3 study study
## 10 KO_33 TRUE TRUE TRUE 3 study study
## 11 KO_72 TRUE TRUE TRUE 3 study study
## 12 KO_14 TRUE TRUE TRUE 3 study study
## 13 KO_48 TRUE TRUE TRUE 3 study study
## 14 KO_71 TRUE TRUE TRUE 3 study study
## 15 KO_28 TRUE TRUE TRUE 3 study study
## 16 KO_22 TRUE TRUE TRUE 3 study study
## 17 KO_59 TRUE TRUE TRUE 3 study study
## 18 KO_15 TRUE TRUE TRUE 3 study study
## 19 KO_74 TRUE TRUE TRUE 3 study study
## # ℹ 5 more variables: variable_aldex2 <chr>, pathway_3 <chr>, pathway_1 <chr>,
## # pathway_2 <chr>, pathway_3_clean <chr>## Rows: 78 Columns: 12
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (8): KO, variable_linda, variable_ancombc, variable_aldex2, pathway_3, p...
## dbl (1): score
## lgl (3): LinDA, ancombc, aldex2
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.6.2 Figure 6
## Rows: 78 Columns: 12
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (8): KO, variable_linda, variable_ancombc, variable_aldex2, pathway_3, p...
## dbl (1): score
## lgl (3): LinDA, ancombc, aldex2
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
## Joining, by = "Sample"
## Joining, by = "OTU"
7 Figure 7: CQI-PMTCT: Correlation between CVMB and immune mediators
## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'## Also defined by 'tidytree'## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'## Also defined by 'tidytree'## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'## Also defined by 'tidytree'## Found more than one class "phylo" in cache; using the first, from namespace 'phyloseq'## Also defined by 'tidytree'## New names:
## Rows: 81 Columns: 69
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," chr
## (11): sampleID, Age_Bin, Trimester, ART_regimen, subCST, CST, Preg_outco... dbl
## (58): ...1, Chao1, Simpson Index, Shannon Entropy, age_years, Gestationa...
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## • `` -> `...1`7.1 A-B.Correlation of MMP2 and CXCL13 to shannon diversity
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
7.2 C:Correlation of L.iners with sCD163
## `geom_smooth()` using formula = 'y ~ x'
7.3 D:Levels of sTNFRSF1A between CST III and IV
## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot
## compute exact p-value with ties
8 Supplemetary Figures
8.1 Fig S2: Heatmap of correlations between all taxa and immune mediators in the CQI-PMTCT cohort
8.2 Fig S3: Covariates explaining microbial variation in the CVMB of the integrated datasets
## [1] "primer_pair" "Trimester" "PCR_cycles"## sampleID continent unsdg_region
## 0.000000 0.000000 0.000000
## country study condition
## 0.000000 0.000000 0.000000
## sequencer sample_loc sample_loc_simple
## 0.000000 0.000000 0.000000
## racioethnicity library_type primer_pair
## 0.000000 0.000000 10.090361
## BMI_Bin Pregnant Trimester
## 0.000000 0.000000 5.873494
## Asymptomatic BV HIV
## 0.000000 0.000000 0.000000
## HPV PCR_cycles Racioethnic_category
## 0.000000 21.385542 0.000000
## Age_Bin health_status CST
## 0.000000 0.000000 0.000000## sampleID continent unsdg_region
## 664 3 2
## country study condition
## 5 6 3
## sequencer sample_loc sample_loc_simple
## 2 4 2
## racioethnicity library_type primer_pair
## 8 1 5
## BMI_Bin Pregnant Trimester
## 1 1 5
## Asymptomatic BV HIV
## 2 1 2
## HPV PCR_cycles Racioethnic_category
## 1 4 6
## Age_Bin health_status CST
## 5 2 5##
## The model is overfitted with no unconstrained (residual) component## Fstat r2 adjr2 pval
## [1,] NA 1 NA NA
## Fstat r2 adjr2 pval
## [1,] NA 1.00000000 NA NA
## [2,] 16.31136 0.04703237 0.04414895 0.001
## Fstat r2 adjr2 pval
## [1,] NA 1.00000000 NA NA
## [2,] 16.31136 0.04703237 0.04414895 0.001
## [3,] 31.13118 0.04491383 0.04347111 0.001
## Fstat r2 adjr2 pval
## [1,] NA 1.00000000 NA NA
## [2,] 16.31136 0.04703237 0.04414895 0.001
## [3,] 31.13118 0.04491383 0.04347111 0.001
## [4,] 20.05330 0.10851160 0.10310044 0.001
## Fstat r2 adjr2 pval
## [1,] NA 1.00000000 NA NA
## [2,] 16.31136 0.04703237 0.04414895 0.001
## [3,] 31.13118 0.04491383 0.04347111 0.001
## [4,] 20.05330 0.10851160 0.10310044 0.001
## [5,] 16.20750 0.10965274 0.10288718 0.001
## Fstat r2 adjr2 pval
## [1,] NA 1.00000000 NA NA
## [2,] 16.31136 0.04703237 0.04414895 0.001
## [3,] 31.13118 0.04491383 0.04347111 0.001
## [4,] 20.05330 0.10851160 0.10310044 0.001
## [5,] 16.20750 0.10965274 0.10288718 0.001
## [6,] 75.48909 0.10235961 0.10100365 0.001
## Fstat r2 adjr2 pval
## [1,] NA 1.00000000 NA NA
## [2,] 16.31136 0.04703237 0.04414895 0.001
## [3,] 31.13118 0.04491383 0.04347111 0.001
## [4,] 20.05330 0.10851160 0.10310044 0.001
## [5,] 16.20750 0.10965274 0.10288718 0.001
## [6,] 75.48909 0.10235961 0.10100365 0.001
## [7,] 11.45409 0.04948751 0.04516700 0.001
## Fstat r2 adjr2 pval
## [1,] NA 1.000000000 NA NA
## [2,] 16.311359 0.047032365 0.044148953 0.001
## [3,] 31.131178 0.044913833 0.043471105 0.001
## [4,] 20.053301 0.108511595 0.103100437 0.001
## [5,] 16.207497 0.109652739 0.102887182 0.001
## [6,] 75.489094 0.102359607 0.101003655 0.001
## [7,] 11.454086 0.049487507 0.045166995 0.001
## [8,] 2.618947 0.003940525 0.002435903 0.009
## Fstat r2 adjr2 pval
## [1,] NA 1.000000000 NA NA
## [2,] 16.311359 0.047032365 0.044148953 0.001
## [3,] 31.131178 0.044913833 0.043471105 0.001
## [4,] 20.053301 0.108511595 0.103100437 0.001
## [5,] 16.207497 0.109652739 0.102887182 0.001
## [6,] 75.489094 0.102359607 0.101003655 0.001
## [7,] 11.454086 0.049487507 0.045166995 0.001
## [8,] 2.618947 0.003940525 0.002435903 0.009
## [9,] 8.540680 0.083523380 0.073743904 0.001
## Fstat r2 adjr2 pval
## [1,] NA 1.000000000 NA NA
## [2,] 16.311359 0.047032365 0.044148953 0.001
## [3,] 31.131178 0.044913833 0.043471105 0.001
## [4,] 20.053301 0.108511595 0.103100437 0.001
## [5,] 16.207497 0.109652739 0.102887182 0.001
## [6,] 75.489094 0.102359607 0.101003655 0.001
## [7,] 11.454086 0.049487507 0.045166995 0.001
## [8,] 2.618947 0.003940525 0.002435903 0.009
## [9,] 8.540680 0.083523380 0.073743904 0.001
## [10,] 15.792706 0.070885202 0.066396724 0.001
## Fstat r2 adjr2 pval
## [1,] NA 1.000000000 NA NA
## [2,] 16.311359 0.047032365 0.044148953 0.001
## [3,] 31.131178 0.044913833 0.043471105 0.001
## [4,] 20.053301 0.108511595 0.103100437 0.001
## [5,] 16.207497 0.109652739 0.102887182 0.001
## [6,] 75.489094 0.102359607 0.101003655 0.001
## [7,] 11.454086 0.049487507 0.045166995 0.001
## [8,] 2.618947 0.003940525 0.002435903 0.009
## [9,] 8.540680 0.083523380 0.073743904 0.001
## [10,] 15.792706 0.070885202 0.066396724 0.001
## [11,] 5.877101 0.042749669 0.035475731 0.001
## Fstat r2 adjr2 pval
## [1,] NA 1.000000000 NA NA
## [2,] 16.311359 0.047032365 0.044148953 0.001
## [3,] 31.131178 0.044913833 0.043471105 0.001
## [4,] 20.053301 0.108511595 0.103100437 0.001
## [5,] 16.207497 0.109652739 0.102887182 0.001
## [6,] 75.489094 0.102359607 0.101003655 0.001
## [7,] 11.454086 0.049487507 0.045166995 0.001
## [8,] 2.618947 0.003940525 0.002435903 0.009
## [9,] 8.540680 0.083523380 0.073743904 0.001
## [10,] 15.792706 0.070885202 0.066396724 0.001
## [11,] 5.877101 0.042749669 0.035475731 0.001
## [12,] 6.757066 0.039398181 0.033567517 0.001
## Fstat r2 adjr2 pval
## [1,] NA 1.000000000 NA NA
## [2,] 16.311359 0.047032365 0.044148953 0.001
## [3,] 31.131178 0.044913833 0.043471105 0.001
## [4,] 20.053301 0.108511595 0.103100437 0.001
## [5,] 16.207497 0.109652739 0.102887182 0.001
## [6,] 75.489094 0.102359607 0.101003655 0.001
## [7,] 11.454086 0.049487507 0.045166995 0.001
## [8,] 2.618947 0.003940525 0.002435903 0.009
## [9,] 8.540680 0.083523380 0.073743904 0.001
## [10,] 15.792706 0.070885202 0.066396724 0.001
## [11,] 5.877101 0.042749669 0.035475731 0.001
## [12,] 6.757066 0.039398181 0.033567517 0.001
## [13,] 66.488563 0.091269193 0.089896488 0.001
## Fstat r2 adjr2 pval
## [1,] NA 1.000000000 NA NA
## [2,] 16.311359 0.047032365 0.044148953 0.001
## [3,] 31.131178 0.044913833 0.043471105 0.001
## [4,] 20.053301 0.108511595 0.103100437 0.001
## [5,] 16.207497 0.109652739 0.102887182 0.001
## [6,] 75.489094 0.102359607 0.101003655 0.001
## [7,] 11.454086 0.049487507 0.045166995 0.001
## [8,] 2.618947 0.003940525 0.002435903 0.009
## [9,] 8.540680 0.083523380 0.073743904 0.001
## [10,] 15.792706 0.070885202 0.066396724 0.001
## [11,] 5.877101 0.042749669 0.035475731 0.001
## [12,] 6.757066 0.039398181 0.033567517 0.001
## [13,] 66.488563 0.091269193 0.089896488 0.001
## [14,] 44.060036 0.211005355 0.206216313 0.001## [1] "13 variables can individually explain part of the (active) microbiome variation in these samples."##
## Some constraints or conditions were aliased because they were redundant. This
## can happen if terms are linearly dependent (collinear): 'studyLivson_2022',
## 'studyMovassagh_2021', 'studyPMTCT_CQI', 'studyServergnini_2021',
## 'sequencernextseq', 'racioethnicityUgandan', 'Trimesterthird',
## 'TrimesterThird', 'sample_locmidvaginal', 'sample_locposterior_fornix',
## 'sample_locvaginal_swab', 'continentEurope', 'continentNorthAm',
## 'unsdg_regionSubSaharanAf', 'Racioethnic_categoryBlack/African American',
## 'Racioethnic_categoryHispanic/Latino', 'Racioethnic_categoryOther',
## 'Racioethnic_categoryUnknown', 'Racioethnic_categoryWhite',
## 'sample_loc_simplevaginal_swab'##
## Some constraints or conditions were aliased because they were redundant. This
## can happen if terms are linearly dependent (collinear): 'studyLivson_2022',
## 'studyMovassagh_2021', 'studyPMTCT_CQI', 'studyServergnini_2021',
## 'sequencernextseq', 'racioethnicityUgandan', 'Trimesterthird',
## 'TrimesterThird', 'sample_locmidvaginal', 'sample_locposterior_fornix',
## 'sample_locvaginal_swab', 'continentEurope', 'continentNorthAm',
## 'unsdg_regionSubSaharanAf', 'Racioethnic_categoryBlack/African American',
## 'Racioethnic_categoryHispanic/Latino', 'Racioethnic_categoryOther',
## 'Racioethnic_categoryUnknown', 'Racioethnic_categoryWhite',
## 'sample_loc_simplevaginal_swab'##
## Some constraints or conditions were aliased because they were redundant. This
## can happen if terms are linearly dependent (collinear): 'countryFinland',
## 'countryItaly', 'countryUganda', 'countryUS'##
## Some constraints or conditions were aliased because they were redundant. This
## can happen if terms are linearly dependent (collinear): 'sequencernextseq'##
## Some constraints or conditions were aliased because they were redundant. This
## can happen if terms are linearly dependent (collinear): 'racioethnicityUgandan'##
## Some constraints or conditions were aliased because they were redundant. This
## can happen if terms are linearly dependent (collinear): 'Trimesterthird',
## 'TrimesterThird'##
## Some constraints or conditions were aliased because they were redundant. This
## can happen if terms are linearly dependent (collinear): 'sample_locmidvaginal',
## 'sample_locposterior_fornix', 'sample_locvaginal_swab'##
## Some constraints or conditions were aliased because they were redundant. This
## can happen if terms are linearly dependent (collinear): 'continentEurope',
## 'continentNorthAm'##
## Some constraints or conditions were aliased because they were redundant. This
## can happen if terms are linearly dependent (collinear):
## 'unsdg_regionSubSaharanAf'##
## Some constraints or conditions were aliased because they were redundant. This
## can happen if terms are linearly dependent (collinear):
## 'sample_loc_simplevaginal_swab'##
## Some constraints or conditions were aliased because they were redundant. This
## can happen if terms are linearly dependent (collinear): 'countryFinland',
## 'countryItaly', 'countryUganda', 'countryUS'##
## Some constraints or conditions were aliased because they were redundant. This
## can happen if terms are linearly dependent (collinear): 'sequencernextseq'##
## Some constraints or conditions were aliased because they were redundant. This
## can happen if terms are linearly dependent (collinear): 'racioethnicityUgandan'##
## Some constraints or conditions were aliased because they were redundant. This
## can happen if terms are linearly dependent (collinear): 'Trimesterthird',
## 'TrimesterThird'##
## Some constraints or conditions were aliased because they were redundant. This
## can happen if terms are linearly dependent (collinear): 'sample_locmidvaginal',
## 'sample_locposterior_fornix', 'sample_locvaginal_swab'##
## Some constraints or conditions were aliased because they were redundant. This
## can happen if terms are linearly dependent (collinear): 'continentEurope',
## 'continentNorthAm'##
## Some constraints or conditions were aliased because they were redundant. This
## can happen if terms are linearly dependent (collinear):
## 'unsdg_regionSubSaharanAf'##
## Some constraints or conditions were aliased because they were redundant. This
## can happen if terms are linearly dependent (collinear):
## 'sample_loc_simplevaginal_swab'## capscale(formula = reduced_spp_filt_n0_clr ~ CST + study + Age_Bin,
## data = metadata_curated_filtered, distance = "euclidean")## R2.adj Df AIC F Pr(>F)
## + CST 0.21093 4 2434.9 42.7019 0.002 **
## + study 0.25837 5 2401.1 8.9315 0.002 **
## + Age_Bin 0.26127 4 2402.6 1.6042 0.016 *
## <All variables> 0.26239
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Warning: `as.tibble()` was deprecated in tibble 2.0.0.
## ℹ Please use `as_tibble()` instead.
## ℹ The signature and semantics have changed, see `?as_tibble`.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.